Designing better ways to design better electronics
Electronic systems are everywhere. They closely interact with the physical systems they are embedded in, with human users, and with other systems connected in networks. Electronic systems are how we are going to create a smart society – if we can solve the societal challenges of reliability, miniaturization and reduction of power dissipation. The Electronic Systems (ES) research group aims to provide a scientific basis for design trajectories for electronic systems, from function to realization, and to do so in collaboration with industry.Read more
The ES research program targets the design of next generations of electronic systems. It is organized in three subprograms that cover the engineering, system and circuit perspectives. This allows an integral approach to the design of electronic systems.
CompSOC Lab- Predictable & Composable Embedded Systems
We research predictable embedded-systems architectures to accelerate verification of real-time performance and safety. To speed up system…
Embedded Control Systems Lab
The ECS Lab focusses on co-design and optimization methods for control systems implemented on resource-constraint embedded platforms. Tool…
Efficient Stream Processing Lab
The Efficient Stream Processing lab focusses on addressing the key challenges in this design flow, i.e. developing adaptive programming…
Model-Based Design Lab
NanoComputing Research Lab
Creating Novel Computing Paradigms – Rethinking computing from materials, devices to architectures by bridging physics, information theory…
Networked Embedded Systems Lab
The Networked Embedded Systems Lab focusses on design, analysis, and optimization of reliable and low-power embedded networks as a main…
Neuromorphic Edge Computing Systems Lab
The Neuromorphic Edge Computing Systems Lab strives to create computing algorithms, circuits, systems, and architectures for emerging…
Signal Processing for Communications Lab
The ubiquity of modern communications systems has revolutionized the way we interact both with other people and with machines
Alarm-Limiting AlgoRithm-based Monitoring
Alarm aims to fuse, process and analyze the unobtrusive vitals and video monitoring of babies to reduce false alarms, monitor motion and…
AquaConnect, key technologies for safeguarding regional water provision in fresh water stressed deltas
A key technology project in the research field of water in the circular economy.
The Brainwave project has received funding from NWO-TTW with the grant number 14714.
We develop real-time hardware & software architecture for (flying, driving) drones such that different modules (applications) can be…
Deep Learning as a Service (DLaaS)
Tooling to improve the usability and efficiency of deep learning for third parties.Several demonstrators will show how to exploit DL as a…
Efficient Deep Learning Platforms (eDLP)
HW-SW design for efficient processing of DNNs on low energy embedded systems
Efficient Deep Learning (EDL)
The EDL program has received funding from NWO-TTW with the grant number P16-25.
FORSEE: Video monitoring for early signaling of adverse events
This project explores continuous video monitoring of the cardiorespiratory status of a patient as an innovative unobtrusive method to reduce…
The oCPS-ITN project has received funding from the European Union’s Horizon 2020 Framework Program for Research and Innovation under grant agreement no 674875.
The REXUS/BEXUS programme allows students from universities and higher education colleges across Europe to carry out scientific and technological experiments on research rockets and balloons.
Scheduling Adaptive Modular Flexible Manufacturing Systems (SAM-FMS)
The overarching goal of the TRANSACT project is to develop a universal, distributed solution architecture for the transformation of…
Research highlight: Machine Learning
Machine learning and in particular deep learning (DL) has dramatically improved the state-of-the-art in object detection, speech recognition, robotics, and many other domains. Whether it is superhuman performance in object recognition or beating human players in Go, the astonishing success of deep learning is achieved by deep neural networks trained with huge amounts of training examples and massive computing resources. Although already applied successfully in academic use-cases and several consumer products (e.g. machine translation), these data and computing requirements pose challenges for further market penetration. Efficient Deep Learning (EDL) is an important focus point of our Smart Electronic Systems and Digital Nanoelectronics subprograms.Read more
Meet some of our Researchers
Majid Nabi Najafabadi
David Simões de Melo
Gerard de Haan
Raquel Pires Alves
Jose Pineda de Gyvez
Alexios Balatsoukas Stimming
Joan Marce i Igual
Erik Jan Marinissen
Manil Dev Gomony
WORK WITH US!
All scientific as well as non-scientific vacancies are centrally cataloged by the Electrical Engineering department and can be found here.
We are always looking for exceptional candidates interested in pursuing a PhD. So if you have (almost completed) a Master's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related area, and if you have expertise and interest in one or more relevant areas, e.g., digital circuits, VLSI, design automation, multiprocessor systems-on-chip, models of computation, compiler technology, embedded systems, IoT, or CPS, you are invited to apply.
Our most recent peer reviewed publications
Imaging Photoplethysmography for Noninvasive Anastomotic Perfusion Assessment in Intestinal SurgeryJournal of Surgical Research (2023)
Simulation and implementation of two-layer oscillatory neural networks for image edge detection: bidirectional and feedforward architecturesNeuromorphic Computing and Engineering (2023)
Intra- and interindividual attack frequency variability of chronic cluster headacheCephalalgia (2023)
Contactless Camera-Based Sleep Staging: The HealthBed StudyBioengineering (2023)
CELR25th Euromicro Conference on Digital Systems Design, DSD 2022 (2023)
Visiting addressFluxGroene Loper 195612 AP EindhovenNetherlands
Postal addressP.O. Box 513Department of Electrical Engineering5600 MB EindhovenNetherlands